Open Access

A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer

Contributed equally
Journal of Translational Medicine201513:231

https://doi.org/10.1186/s12967-015-0556-3

Received: 2 March 2015

Accepted: 1 June 2015

Published: 17 July 2015

Abstract

Background

Accumulated evidence suggests that dysregulated expression of long non-coding RNAs (lncRNAs) may play a critical role in tumorigenesis and prognosis of cancer, indicating the potential utility of lncRNAs as cancer prognostic or diagnostic markers. However, the power of lncRNA signatures in predicting the survival of patients with non-small cell lung cancer (NSCLC) has not yet been investigated.

Methods

We performed an array-based transcriptional analysis of lncRNAs in large patient cohorts with NSCLC by repurposing microarray probes from the gene expression omnibus database. A risk score model was constructed based on the expression data of these eight lncRNAs in the training dataset of NSCLC patients and was subsequently validated in other two independent NSCLC datasets. The biological implications of prognostic lncRNAs were also analyzed using the functional enrichment analysis.

Results

An expression pattern of eight lncRNAs was found to be significantly associated with overall survival (OS) of NSCLC patients in the training dataset. With the eight-lncRNA signature, patients of the training dataset could be classified into high- and low-risk groups with significantly different OS (median survival 1.67 vs. 6.06 years, log-rank test p = 4.33E−09). The prognostic power of eight-lncRNA signature was further validated in other two non-overlapping independent NSCLC cohorts, demonstrating good reproducibility and robustness of this eight-lncRNA signature in predicting OS of NSCLC patients. Multivariate regression and stratified analysis suggested that the prognostic power of the eight-lncRNA signature was independent of clinical and pathological factors. Functional enrichment analyses revealed potential functional roles of the eight prognostic lncRNAs in tumorigenesis.

Conclusions

These findings indicate that the eight-lncRNA signature may be an effective independent prognostic molecular biomarker in the prediction of NSCLC patient survival.

Keywords

Long non-coding RNANon-small cell lung cancerOverall survivalSignature

Background

Lung cancer is one of the most common human cancers and is the leading cause of cancer-related deaths among both men and women globally [1], accounting for about 27% of all cancer-related deaths. In China, lung cancer has become the primary cause of cancer-related deaths, and mortality has increased by more than four times during the past three decades [2]. The overall 5-year relative survival rate for lung cancer is low at nearly 15%, which is primarily due to principal detection at late, incurable stages and a paucity of late-stage treatments [3]. Lung cancer is generally divided into two main categories: small cell lung cancer and non-small cell lung cancer (NSCLC) accounting for approximately 80% of all lung cancers.

Long non-coding RNAs (lncRNAs), a recently discovered subclass of non-coding RNA (ncRNA), are most commonly defined as RNA transcripts longer than 200 nucleotides with little coding capacity [4, 5]. Though the functions of only a limited number of lncRNAs have been well characterized, accumulating evidence has suggested that lncRNAs participate in a wide variety of biological processes, including cell differentiation, organogenesis, chromatin modification, genomic imprinting, dosage compensation, respond to diverse stimuli and so on, by exerting their functions as four archetypes: signals, decoys, guides and scaffolds [6, 7]. lncRNAs can regulate gene expression at the post-transcriptional level via competing endogenous RNA (ceRNA) crosstalk or at the transcriptional level via cis or trans and at the epigenetic regulation level [810]. Recently, a number of cancer-related studies have detected many dysregulated lncRNAs associated with tumorigenesis and tumor progression in a variety of cancers [1113]. Like protein-coding genes and miRNA, some dysregulated lncRNAs play oncogene-like roles. For instance, HOTAIR is an lncRNA that is overexpressed in breast tumors and significantly associated with breast cancer metastasis [14]. Overexpression of lncRNA PCAT-1 is associated with poor prognosis in patients with colorectal cancer (CRC) [15]. Other well-studied lncRNAs, such as MEGS, GAS5, LIN00312 and LinRNA-p21, have instead demonstrated tumor suppressive roles [16, 17]. For example, lncRNA LIN00312, which is significantly down-regulated in nasopharyngeal carcinoma (NPC), was found to be an independent contributor to NPC [18]. These findings suggest that, like protein-coding genes and miRNAs, lncRNAs could serve as diagnostic and prognostic biomarkers. Li et al. [19] measured lncRNA expression in paired tumors and adjacent normal tissues of 119 patients and identified a three-lncRNA signature that could predict the survival of patients with oesophageal squamous cell carcinoma (OSCC). Recent studies have also demonstrated emerging roles of lncRNAs in NSCLC [20]. For example, lncRNA MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) is up-regulated in NSCLC based on evidence from subtractive hybridization of cDNA libraries, and can be used as an independent prognostic marker of patient survival [21]. White and colleagues [22] found 111 differentially expressed lncRNAs between lung tumors and adjacent normal tissues, some of which have been functionally validated to be involved in cellular proliferation in vitro. Nie et al. [23] identified an lncRNA MVIH which is over-expressed in NSCLC tissues compared with adjacent normal tissues. Subsequent studies, integrating custom-designed gene microarray and clinical information, also discovered lncRNA signatures that were significantly associated with the survival of patients with gliolastoma multiforme [24], colorectal cancer [25] and breast cancer [26]. Other recent studies have characterized tens of lncRNAs that were identified to be associated with the presence of certain lung cancer histological subtypes [27, 28]. While the prognostic power of mRNA and miRNA signatures in various cancers is well established, the power of lncRNA signatures in predicting the survival of patients with NSCLC has not yet been investigated.

In the present study, we conducted a comprehensive study of lncRNA expression profiles across 603 NSCLC patients with clinical information by repurposing the previously published NSCLC gene expression profiles, and identified an eight-lncRNA signature associated with survival. A risk score formula was constructed based on the expression data of these eight lncRNAs in the training dataset of NSCLC patients and was further confirmed in another two independent gene expression omnibus (GEO) NSCLC patient cohorts.

Methods

NSCLC datasets and patient information

NSCLC microarray datasets, generated with the Affymetrix platform (HG-U133A Plus 2.0), and clinical information were obtained from the GEO database. After removing the patients without available survival information, a total of 603 patients were enrolled in this study (Table 1), including 196 patients from GSE37745 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37745) [29], 226 patients from GSE31210 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31210) [30] and 181 patients from GSE50081 (www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50081) [31]. More detailed clinical information of all 603 NSCLC patients included in this study can be found in Table 1.
Table 1

Clinical features of all 603 NSCLC patients included in this study

Features

GSE37745 (n = 196)

GSE31210 (n = 226)

GSE50081 (n = 181)

Age (years), no (%)

 ≤65

94 (48.0)

176 (77.9)

59 (32.6)

 >65

102 (52.0)

50 (22.1)

122 (67.4)

Gender, no (%)

 Male

107 (55.0)

105 (46.5)

98 (54.1)

 Female

89 (45.0)

121 (53.5)

83 (45.9)

Vital status (%)

 Alive

52 (26.5)

191 (84.5)

106 (58.6)

 Dead

144 (73.5)

35 (15.5)

75 (41.4)

Disease stage, no (%)

 I

130 (66.0)

168 (74.3)

127 (70.2)

 II

35 (18.0)

58 (25.7)

54 (29.8)

 III

27 (14.0)

  

 IV

4 (2.0)

  

Smoking status

 Never-smoker

 

115 (50.9)

24 (13.3)

 Ever-smoker

 

111 (49.1)

79 (43.6)

 Current

  

54 (29.8)

 Undetermined

  

21 (11.6)

Histology

 Adenocarcinoma

106 (54.0)

226

128 (70.7)

 Large cell carcinoma

24 (12.0)

 

7 (3.9)

 Squamous cell carcinoma

66 (34.0)

 

43 (23.9)

Microarray processing and lncRNA profile mining

All the microarray raw data (.CEL files) of three NSCLC cohorts were obtained from the GEO database and processed using the robust multichip average (RMA) algorithm for background adjustment [32, 33]. The Affymetrix GeneChip probe-level data was log-2-scale transformed and standardized by transforming the expression data into having a mean of 0 and a standard deviation (SD) of 1. The NetAffx probe set sequences for Affymetrix HG-U133 Plus 2.0 were downloaded from the Affymetrix website (http://www.affymetrix.com). LncRNA expression data from the Affymetrix-based expression profiling of NSCLC cohorts were obtained by repurposing microarray probes based on the sequences of the probe sets and the annotations of lncRNAs in GENCODE (http://www.gencodegenes.org/) (GRCh38, release 21) [34], as previous described [35]. By keeping probes that were uniquely mapped to the genomic coordinate of lncRNAs derived from GENCODE, 3,521 probes (or probe sets) and 2,313 corresponding lncRNA genes were obtained. Multiple probes (or probe sets) mapping to the same gene were integrated by using the arithmetic mean of the values of multiple probes (or probe sets) to generate a single gene expression value (on the log2 scale).

Statistical analysis

A univariable Cox regression analysis was performed to evaluate the relationship between the continuous expression level of each lncRNA and patients’ overall survival (OS) in the training dataset. Only those lncRNAs with a p value of <0.005 were considered statistically significant. To construct a predictive model, each of the selected lncRNA genes was analyzed using a multivariable Cox regression model in the training dataset, with OS as the dependent variable and other clinical information as the covariables. A risk score was then computed as follows:
$$ Risk\;Score\;(RS) = \sum\limits_{i = 1}^{N} {\left( {Exp_{i} * Coe_{i} } \right)} $$
where \( N \) is the number of prognostic lncRNA genes, \( Exp_{i} \) is the expression value of \( \ln cRNA_{i} \), and \( Coe_{i} \) is the estimated regression coefficient of \( \ln cRNA_{i} \) in the multivariable Cox regression analysis. This risk score model was established by taking into account the power of each of the prognostic lncRNA genes.

Using the median risk score in the training dataset as a cutoff value, NSCLC patients in each dataset were divided into high- and low-risk groups. Kaplan–Meier survival analyses were performed to test the equality for survival distributions in different groups for each NSCLC cohort, and statistical significance was assessed using the two-sided log-rank test. Additionally, a multivariable Cox regression analysis and data stratification analysis were performed to test whether the risk score was independent of other clinical features within the available data. The time-dependent receiver operating characteristic (ROC) curve was also used to compare the sensitivity and specificity of the survival prediction of the lncRNA expression-based risk score in the training dataset. Area under the curve (AUC) value was calculated from the ROC curve. All analyses were performed using R software and Bio-conductor. Significance was defined as p < 0.05.

Bioinformatics analysis of lncRNA gene function prediction

The co-expressed relationships between the prognostic lncRNAs and protein-coding genes were computed using Pearson correlation coefficients. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses of the co-expressed protein-coding genes with prognostic lncRNAs were performed to predict the biological function of prognostic lncRNAs using the DAVID Bioinformatics Tool (version 6.7), which is a commonly used functional annotation tool that can assess over-representation of functional categories among a gene set of interest [37]. Enrichment analysis was carried out using the functional annotation chart and functional annotation clustering options, and was limited to KEGG pathways and GO terms in the “Biological Process” categories. Functional annotation with p value of <0.05 and an enrichment score of >2 were considered significant.

Results

Derivation of an eight-lncRNA prognostic signature from the training dataset

The NSCLC patient cohort from GSE37745 (n = 196), including the relatively large patient sample size and relatively overall clinical information, was selected as training dataset to explore the association between lncRNA expression and OS of NSCLC patients. We first conducted a univariate Cox proportional hazards regression analysis of the lncRNA expression data with OS as the dependent variable, and identified a set of eight lncRNAs as prognostic lncRNAs which were significantly correlated with patients’ OS (p value of <0.005). Table 2 shows a list of these eight prognostic lncRNAs along with important variable information. Of the eight lncRNAs, the higher expression level of lncRNA RP11-21L23.2, GPR158-AS1, RP11-701P16.5 and RP11-379F4.4 was associated with shorter OS (coefficient >0), and the higher expression levels of the remaining four lncRNAs (CTD-2358C21.4, RP11-94L15.2, KCNK15-AS1 and AC104134.2) were associated with longer OS (coefficient < 0). Then we further examined whether these eight prognostic lncRNAs are differentially expressed between cancer and normal lung tissue. The lncRNA differential expression analysis was performed for GSE18842 dataset (including 46 tumor and 45 normal lung tissue specimens) (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18842) [38] obtained from GEO database. We found that five of eight prognostic lncRNAs showed significant expression differences between tumor and normal lung tissue (Mann–Whitney U test p < 0.05) (Additional file 1: Figure S1), demonstrating that these selected prognostic lncRNAs are associated with lung cancer.
Table 2

Eight lncRNAs significantly associated with the overall survival of NSCLC patients in the training set (n = 196)

Ensembl id

Gene symbol

Chromosomal position

P valuea

Hazard ratioa

Coefficienta

ENSG00000261578.1

RP11-21L23.2

Chr11: 76,800,364-76,804,555(+)

2.57E−05

1.374

0.318

ENSG00000261731.2

CTD-2358C21.4

Chr16: 31,709,113-31,711,984(−)

2.22E−04

0.725

−0.322

ENSG00000264198.2

RP11-94L15.2

Chr17: 39,757,715-39,763,836(−)

2.88E−04

0.738

−0.303

ENSG00000233642.1

GPR158-AS1

Chr10: 25,158,072-25,176,276(−)

4.52E−04

1.334

0.288

ENSG00000244558.3

KCNK15-AS1

Chr20: 44,694,892-44,746,021(−)

1.27E−03

0.750

−0.287

ENSG00000225420.1

AC104134.2

Chr2: 88,538,720-88,575,610 (+)

1.85E−03

0.760

−0.274

ENSG00000251230.3

RP11-701P16.5

Chr4: 184,844,585-184,855,751(−)

2.01E−03

1.301

0.263

ENSG00000240207.4

RP11-379F4.4

Chr3: 158,732,263-158,784,070(+)

2.80E−03

1.433

0.360

aDerived from the univariable Cox regression analysis in the training set.

An eight-lncRNA signature predicts survival of NSCLC patients in the training dataset

To investigate whether the eight-lncRNA signature could provide an accurate prediction of OS in NSCLC patients, the expression data of these eight lncRNAs and other clinical features were fitted into a multivariable Cox regression model as covariates of the training dataset. A risk score was generated for each patient in the training dataset according to the risk-score model (see “Methods”) as follows: Risk score = (0.306 × expression value of RP11-21L23.2) + (−0.314 × expression value of CTD-2358C21.4) + (−0.252 × expression value of RP11-94L15.2) + (0.288 × expression value of GPR158-AS1) + (−0.271 × expression value of KCNK15-AS1) + (−0.299 × expression value of AC104134.2) + (0.284 × expression value of RP11-701P16.5) + (0.321 × expression value of RP11-379F4.4). To evaluate how well the risk score predicts the 5-year survival, the various cutoff values were evaluated using time-dependent ROC curve (Figure 1a) which is commonly used for revealing the predictive accuracy of a model [39, 40]. In the training dataset, AUC for the eight-lncRNA signature prognostic model was 0.78 at an OS of 5 years, demonstrating the better performance for survival prediction of the lncRNA expression-based risk score in the training dataset. All patients in the training dataset were then ranked according to their risk score, and divided into either the high- or low-risk group using the median risk score as the cutoff point. According to this cutoff value, patients were divided into either a high-risk group (n = 98) or a low-risk group (n = 98). Patients in the high-risk group had a significantly shorter OS than those in the low-risk group (median OS 1.67 vs. 6.06 years, log-rank test p = 4.33E−09). Kaplan–Meier curves for the high- and low-risk groups in the training dataset (n = 196) are shown in Figure 1b. In detail, OS rates of patients in the high-risk group were 30.6% at 4 years, 19.1% at 6 years, 17.8% at 8 years and 11.9% at 10 years, versus 63.3, 53.8, 46.3 and 38% in the low-risk group, respectively.
Figure 1

The eight-lncRNA signature-focused risk score in prognosis of overall survival in the GSE37745 patient set. a Receiver operating characteristic (ROC) analysis of the risk scores for overall survival prediction in the training dataset. The area under the curve (AUC) was calculated for ROC curves, and sensitivity and specificity were calculated to assess score performance. b The Kaplan–Meier curve for overall survival of two patient groups with high- and low-risk scores in the GSE37745 training set (n = 196). The differences between the two curves were evaluated by the two-sided log-rank test. c The eight lncRNA-based risk score distribution, patients’ survival status and heatmap of the eight lncRNA expression profiles. The black dotted line represents the cutoff value of the risk score derived from the training set which separated patients into high- and low-risk groups.

A significant association between the eight-lncRNA signature risk score and OS was observed in the univariable Cox regression model (Table 3). The hazard ratios of the eight-lncRNA signature risk score of the high-risk group versus that of the low-risk group for OS was 2.641 [p < 0.001; 95% confidence interval (CI) 1.887–3.697; Table 3].
Table 3

Univariable and multivariable Cox regression analysis of the lncRNA signature and overall survival of NSCLC patients in the training and two independent cohorts

Variables

Univariable analysis

Multivariable analysis

HR

95% CI of HR

P value

HR

95% CI of HR

P value

GSE37745 training set, n = 196

 Eight-lncRNA risk score

  Low risk/high risk

2.641

1.887–3.697

<0.001

2.761

1.934–3.942

<0.001

 Age

  ≤65/>65

1.355 

0.977–1.878

0.069

1.427 

1.024–1.986

0.035

 Gender

  Female/male

1.096

0.789–1.523

0.585

0.913 

0.640–1.303

0.616

 Stage

  I

1 (reference)

  

1 (reference)

  

  II

1.220

0.793–1.875

0.366

1.169

0.758–1.802

0.479

  III

1.864

1.187–2.928

0.007

1.656

1.052–2.608

0.029

  IV

1.313

0.415–4.152

0.643

1.566

0.488–5.022

0.451 

 Subtype

  Adenocarcinoma

1 (reference)

  

1 (reference)

  

  Large cell carcinoma

0.891

0.520–1.528

0.675

0.782

0.449–1.360

0.383

  Squamous cell carcinoma

1.257

0.883–1.791

0.205

0.920

0.623–1.359

0.676

GSE31210 testing set, n = 226

 Eight-lncRNA risk score

  Low risk/High risk

3.067

1.471–6.395

0.003

2.643

1.263–5.528

0.010

 Age

  ≤65/>65

2.584

1.313–5.083

0.006

3.685

1.800–7.544

<0.001

 Gender

  Female/male

1.519

0.780–2.955

0.219

1.143

0.402–3.246

0.802

 Smoking status

  No/Yes

1.637

0.837–3.201

0.15

1.388

0.482–3.996

0.544

 Stage

  I/II

4.232

2.175–8.236

<0.001

4.363

2.161–8.811

<0.001

GSE50081 testing set, n = 181

 Eight-lncRNA risk score

  Low risk/high risk

1.795

1.127–2.859

0.014

1.752

1.014–3.026

0.044

 Age

  ≤65/>65

1.559

0.932–2.608

0.090

1.316

0.752–2.303

0.336

 Gender

  Female/male

1.934

1.190–3.143

0.008

1.743

1.011–3.005

0.046

 Smoking status

  No/Yes

1.387

0.659–2.916

0.389

1.054

0.476–2.333

0.897

 Stage

  I/II

1.689

1.049–2.718

0.031

2.359

1.379–4.034

0.002

 Subtype

  Adenocarcinoma

1 (reference)

  

1 (reference)

  

  Large cell carcinoma

1.326

0.479–3.671

0.587

1.094

0.376–3.184

0.870

  Squamous cell carcinoma

0.791

0.456–1.371

0.403

0.479

0.241–0.952

0.036

The distribution of risk score, survival status and prognostic lncRNA expression in 196 patients of the training dataset are shown in Figure 1c. Of these eight prognostic lncRNAs, the high expression level of lncRNA RP11-21L23.2, GPR158-AS1, RP11-701P16.5 and RP11-379F4.4 was associated with high risk, while the remaining four lncRNAs (CTD-2358C21.4, RP11-94L15.2, KCNK15-AS1 and AC104134.2) were shown to be protective. NSCLC patients with high prognostic scores tended to express high-risk lncRNAs, whereas those with low prognostic scores tended to express protective lncRNAs. Moreover, more deaths were noted for NSCLC patients with high-risk scores than for those with low-risk scores.

Validation of the eight-lncRNA signature for survival prediction in the testing GSE31210 dataset

To validate the prognostic power of the eight-lncRNA signature for survival prediction, the constructed expression-defined lncRNA prognostic model was also evaluated in the testing GSE31210 dataset. The same prognostic risk score model obtained from the training dataset was used to calculate the eight-lncRNA signature-based risk scores for 226 patients in the entire GSE31210 dataset. The cutoff value of the risk score derived from the training dataset without re-estimating parameters was used for the testing dataset to classify the patients into either a high-risk group (n = 111) or a low-risk group (n = 115). Patients with high-risk scores exhibited poorer OS than those with low-risk scores (median OS 4.45 vs. 5.08 years, log-rank test p = 1.65E−03). Kaplan–Meier curves for the high- and low-risk groups in the testing dataset are shown in Figure 2a. The OS rate of patients in the high-risk group was 91.7% at 2 years and 78.7% at 4 years, versus 97.4 and 91.5% in the low-risk group, respectively. A significant association between the eight-lncRNA signature risk score and OS in the univariable Cox regression model was observed. The hazard ratios of the eight-lncRNA signature risk scores of the high-risk group versus the low-risk group for OS was 3.067 (p = 0.003; 95% CI 1.471–6.395; Table 3).
Figure 2

The eight-lncRNA signature-focused risk score in prognosis of overall survival in additional validation datasets. a Kaplan–Meier survival curves were plotted for GSE31210 (n = 226). b The eight lncRNA-based risk score distribution, patients’ survival status and heatmap of the eight lncRNA expression profiles were analyzed in the GSE31210. c Kaplan–Meier survival curves were plotted for GSE50081 (n = 181). d The eight lncRNA-based risk score distribution, patients’ survival status and heatmap of the eight lncRNA expression profiles were analyzed in the GSE50081.

The distribution of patient lncRNA risk score, survival status and prognostic lncRNA expression in 226 patients of the GSE31210 dataset are shown in Figure 2b, revealing the similar results observed in the GSE37745 training dataset.

Further validation of the eight-lncRNA signature in another independent dataset

To investigate the reproducibility of the eight-lncRNA signature in predicting OS of NSCLC patients, the prognostic power of the eight-lncRNA signature for prediction of survival was further validated in another independent NSCLC cohort of 181 patients whose expression and survival data were obtained from GEO GSE50081. The clinical feature of this independent NSCLC cohort is shown in Table 1. Patients in this independent NSCLC cohort were classified into either a high-risk group (n = 90) or a low-risk group (n = 91) according to the cutoff value of risk scores obtained from the training dataset. The median OS of the high-risk group for the GSE50081 dataset is 4.29 years, whereas that of the low-risk group is 4.99 years (log-rank test p = 1.26E−02). Kaplan–Meier curves for the high- and low-risk groups within the independent GSE50081 cohort is shown in Figure 2c. Further univariable Cox regression analysis revealed that the high-risk scores of eight-lncRNA signature was significantly associated with lower OS of patients in GSE50081 dataset (p = 1.40E−02; HR = 1.795, 95% CI 1.127–2.859; Table 3). Figure 2d shows the distribution of patient risk scores, the survival status and prognostic lncRNA expression in the independent GSE50081 NSCLC cohort, ranked according to the prognostic risk score values for the eight-lncRNA signature, which were similar to those observed in the training and GSE31210 datasets.

Survival prediction by the eight-lncRNA signature is independent of clinical features

To assess whether the prognostic power of the eight-lncRNA signature for prediction of survival was independent of other clinical features, multivariable Cox regression analysis was performed using the lncRNA signature-based risk score and other clinical features, including age, gender, smoking status, tumor stage and subtype, which were used as covariates. The results of multivariable Cox regression analysis from three NSCLC patients datasets showed that the prognostic power of the eight-lncRNA signature risk score (high-risk group vs. low-risk group, HR = 2.761, 95% CI 1.934–3.942, p < 0.001 for GSE37745; HR = 2.643, 95% CI 1.263–5.528, p = 0.01 for GSE31210; HR = 1.752, 95% CI 1.014–3.026, p = 0.044 for GSE50081) for prediction of survival was indeed independent of these clinical features (Table 3). We also found that the two clinical factors, age and stage, also affected overall survival of patients. So, a data stratification analysis was performed according to age and stage. The three GEO datasets (GSE37745, GSE31210 and GSE50081), which included a total of 603 patients, were stratified by age into either a younger stratum (age ≤65) or an elder stratum (age >65). The results of stratified analysis showed effective prognostic power in both the younger and elder patient groups. The eight-lncRNA signature could classify patients within each age stratum into either high- or low-risk groups with significantly different OS (log-rank test p = 4.46E−05 for the younger patient group and p = 6.61E−06 for the elder patient group) (Figure 3a, b), which suggested that the prognostic power of the eight-lncRNA signature was also age-independent. Then the patients of early (stage I and II) and late (III and IV) stage for GSE37745 dataset were grouped into two separate groups. The stratified analysis was further performed in early and late stage patients to evaluate whether the eight-lncRNA signature could predict survival of patients for different clinical stage. The log-rank test of early stage patients showed that within stage I and II, the eight-lncRNA signature could further subdivide them into either a high-risk group with shorter survival or a low-risk group with longer survival (median OS 2.03 vs. 8.05 years, log-rank test p = 7.81E−09) (Figure 3c). Difference for OS between high-risk group (n = 18) and low-risk group (n = 13) also was observed for late stage patients (median OS 0.975 vs. 3.367 years) (Figure 3d), although the log-rank p value is 0.253 which was above the 0.05 significance level.
Figure 3

Survival analyses of all patients with available age or tumor stage information using the eight-lncRNA signature. a Kaplan–Meier survival curves for younger patients with NSCLC (age ≤65, n = 337). b Kaplan–Meier survival curves for elder patients with NSCLC (age >65, n = 226). c Survival prediction in early stage patients: Kaplan–Meier survival curves for all patients with stage I and II (n = 165). d Survival prediction in late stage patients: Kaplan–Meier survival curves for all patients with stage III and IV (n = 31).

Functional characterization of the eight prognostic lncRNAs

To further investigate the potential biological roles involving the eight prognostic lncRNAs, the co-expressed relationships between the expression of eight lncRNAs and those of the protein-coding genes were computed using Pearson correlation coefficients in the GSE37745 dataset of 196 patients. The expression of 679 protein-coding genes were highly correlated with that of at least one of the eight signature lncRNAs (Pearson correlation coefficient >0.40). GO and KEGG pathway function enrichment analysis for these co-expressed protein-coding genes was then performed, using the whole human genome as the background. The results showed that four genes (GATA6, CRISPLD2, CFTR2 and CLPTM1L) have been proven to be involved in lung cancer. GO functional annotation suggested that 679 protein-coding genes were significantly enriched in 28 GO terms (Figure 4a). These significant GO terms were organized into an interaction network with similar functions using the Enrichment Map [41] plugin in Cytoscape [42]. Several clusters of functionally related GO terms were observed including organ development and cell proliferation and immune, response to stimulus, catabolic and metabolic process (Figure 4b). Taken together, these results implied that the eight lncRNAs might be involved in tumorigenesis through interacting with protein-coding genes that affect the tissue/organ development and other important biological processes.
Figure 4

Functional enrichment results of the co-expressed protein-coding genes with prognostic lncRNAs. a Significantly enriched GO terms of the co-expressed protein-coding genes with prognostic lncRNAs. b The functional enrichment map of GO terms. Each node represents a GO term, which are grouped and annotated by GO similarity. A link represents the overlap of shared genes between connecting GO terms. Node size represents the number of gene in the GO terms. Color intensity is proportional to enrichment significance.

Discussion

During the past few decades, considerable efforts have been made toward the development of gene-expression-based diagnostic and prognostic biomarkers for lung cancer at the protein-coding genes and miRNAs levels [43, 44]. However, accumulating evidence suggested that lncRNA are involved in oncogenic and tumor suppressive pathways have opened the door for this new biomarker. Transcriptional profiling analyses have discovered a number of tissue-specific lncRNAs in normal tissues and dysregulated lncRNAs in a variety of human cancers [11, 45], and highly aberrant expression of dysregulated lncRNAs is associated with tumorigenesis [17]. Furthermore, these dysregulated lncRNAs have already shown great potential as novel molecular biomarkers for diagnosis, prognosis and treatment of cancer. More recently, several studies conducted array-based transcriptional analyses of lncRNAs and functionally characterized cancer subtype-associated lncRNAs in breast cancer and lung cancer, proposing a novel clinical implication for lncRNAs as valuable biomarkers for prediction of response to treatment as well as patient outcome [27, 46]. Compared to protein-coding genes, the advantage of lncRNAs as molecular biomarkers is that lncRNA expression is more closely associated with its biological function and tumor status [16, 47]. However, to date, expression profile-based prognostic lncRNA signature for prediction of survival of NSCLC patients has not been investigated.

Recently, several studies have reported that lncRNA expression profiles can be obtained from publicly available, custom-designed DNA microarrays by re-annotating the array probes [19, 25, 26, 35, 47]. In this study, microarray probe re-annotation was used to repurpose the publicly available human Affymetrix microarray data (HG-U133 Plus 2.0) and subsequently obtain lncRNA expression profiles of 603 NSCLC patients from GEO. To identify lncRNAs with prognostic value in NSCLC, survival analysis was performed by integrating lncRNA expression profiles and clinical information in a large cohort of NSCLC patients. An expression pattern of eight lncRNAs was found to be significantly associated with OS of NSCLC patients in the GSE37745 training dataset. Further ROC analysis demonstrated good performance for predicting 5-year OS. A prognostic risk score model was developed based on the expression data of these eight lncRNAs and weighted by the estimated regression coefficients from multivariable Cox regression analysis. With this eight-lncRNA signature, patients in the training dataset with high-risk scores tended to have lower OS than those with low-risk scores. The separation between survival curves for high- and low-risk patients of the training dataset used for model development was observed. A previous simulation study revealed that a prognostic model can also be developed that is significantly associated with survival time in the training dataset when using completely random gene expression profiles [48]. To evaluate the robustness and reproducibility of the prognostic power of the eight-lncRNA signature, it was also tested in the non-overlapping two other independent NSCLC patient cohorts (GSE31210 and GSE50081) using the same model and criteria as those from the training dataset. In these tests, the prognostic power was also strong, indicating that the eight-lncRNA signature demonstrated good reproducibility and robustness for the NSCLC patients.

Several studies have observed different clinical characteristics and survival time among different age groups of NSCLC patients [4951]. Multivariable Cox regression analysis was thus used to assess the independence of the eight-lncRNA signature in predicting OS. With age, gender, smoking status, stage and subtype as covariables in the regression analysis, risk score of the eight-lncRNA signature was found to have maintained an independent correlation with OS. In the stratified analysis, the eight-lncRNA signature showed prognostic power for different age groups, in which patients belonging to the same age group could be classified into high- and low-risk groups with significantly different survival prospects, indicating that the prognostic value of the eight-lncRNA signature was independent of age of the NSCLC patients. In lung cancer, clinicopathological parameters like tumor histology, staging and localization of metastases determine patients’ outcome [52]. Since tumor stage and subtype data was only available for the GSE37745 patient dataset, multivariate Cox regression analysis and stratified analysis were performed to assess the stage- and subtype-independence of prognostic power of the eight-lncRNA signature. The eight-lncRNA signature was indeed found to be stage-dependent in NSCLC patients, and its prognostic power was significant in stage I and II patients, in which all patients in stage I and II could be separated into high- and low-risk groups with significantly different survival. However, the eight-lncRNA signature achieved a p value of 0.253 for OS prediction of late stage patients, which was above the 0.05 significance level, suggesting that patients with early stage cancer may benefit significantly from this eight-lncRNA prognostic signature. Further multivariate Cox regression analysis testing tumor subtype-independence suggested that prognostic power of the eight-lncRNA signature is independent of tumor subtype. Taken together, these results suggest that the prognostic power of the eight-lncRNA signature for predicting OS of NSCLC patients is independent of other clinical features except for stage.

Tens of thousands of lncRNAs have been identified and predicted by large-scale transcriptome analysis in humans [53]. However, the functions of only a few lncRNAs have been well characterized, so no thorough functional annotation data is available for the eight prognostic lncRNAs in the current literature. Recent bioinformatics studies have suggested that the function of lncRNAs could effectively be predicted with the inclusion of different kinds of biological data. To increase our understanding of the biological roles of the eight prognostic lncRNAs in NSCLC, functional enrichment analysis was performed for 679 protein-coding genes co-expressed with the eight prognostic lncRNAs at the GO and KEGG pathway level. The biological processes most highly associated with the genes were organ development, cell proliferation and immune, response to stimulus, catabolic and metabolic process. In particular, several co-expressed protein-coding genes with eight prognostic RNAs were proven to participate in the NSCLC pathway. These results implied important functional roles of the eight prognostic lncRNAs in tumorigenesis.

Due to the restriction of available data, gene expression profiles of only 2,313 of the tens-of-thousands of known and predicted lncRNAs were obtained. However, the prognostic power of the eight-lncRNA signature uncovered in this study for predicting OS consistently observed in multiple independent datasets. Moreover, the incompleteness and low coverage of available lncRNA-related datasets are common when by studying lncRNA-disease associations. Although the functions of these eight lncRNAs have been inferred by bioinformatics analysis, the biological roles of these eight lncRNAs in tumorigenesis are still not clear and should be investigated in further experimental studies. With the rapid increase of lncRNA-related studies, more comprehensive lncRNA will become available, and lncRNA biomarker development for clinical prognostic evaluation of NSCLC should increase.

Conclusions

In summary, by examining lncRNA expression profiles of patients with NSCLC, our study identified eight lncRNAs associated with overall survival of NSCLC patients. A prognostic lncRNA signature was developed based on the expression patterns of these eight lncRNAs in the training dataset to predict the overall survival, and subsequently was validated in other two independent datasets. Further analysis demonstrated that the prognostic power of the eight-lncRNA signature for prediction of survival was independent of other clinical features. Our results suggested that the eight-lncRNA signature may be an effective independent prognostic molecular biomarker in the prediction of NSCLC patient survival.

Notes

Abbreviations

AUC: 

area under the ROC curve

CeRNA: 

competing endogenous RNA

CI: 

confidence interval

CRC: 

colorectal cancer

GEO: 

gene expression omnibus

GO: 

gene ontology

HR: 

hazard ratio

KEGG: 

Kyoto encyclopedia of genes and genomes

LncRNAs: 

long non-coding RNAs

MALAT1: 

metastasis-associated lung adenocarcinoma transcript 1

NPC: 

nasopharyngeal carcinoma

NSCLC: 

non-small cell lung cancer

OS: 

overall survival

OSCC: 

oesophageal squamous cell carcinoma

RMA: 

robust multichip average

ROC: 

receiver operating characteristic

SD: 

standard deviation

Declarations

Authors’ contributions

JS and DPH conceived and designed the experiments. MZ, MNG, DFH, XJW, YQC and HXY analyzed data. MZ and JS wrote this manuscript. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by Industrial Technology Research and Development Project, Jilin Province (Grant No. 2013C014-3) and the National Natural Science Foundation of China (Grant No. 61403111). The authors thank the seven anonymous reviewers for their constructive comments and suggestions for improving the manuscript.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
College of Bioinformatics Science and Technology, Harbin Medical University
(2)
Department of Interventional Radiology, The Affiliated Tumor Hospital of Harbin Medical University
(3)
School of Life Sciences, Jilin University

References

  1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90PubMedView ArticleGoogle Scholar
  2. She J, Yang P, Hong Q, Bai C (2013) Lung cancer in China: challenges and interventions. Chest 143(4):1117–1126PubMedView ArticleGoogle Scholar
  3. Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K et al (2008) Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455(7216):1069–1075PubMed CentralPubMedView ArticleGoogle Scholar
  4. Cheetham SW, Gruhl F, Mattick JS, Dinger ME (2013) Long noncoding RNAs and the genetics of cancer. Br J Cancer 108(12):2419–2425PubMed CentralPubMedView ArticleGoogle Scholar
  5. Ma L, Bajic VB, Zhang Z (2013) On the classification of long non-coding RNAs. RNA Biol 10(6):925–933PubMedView ArticleGoogle Scholar
  6. Fatica A, Bozzoni I (2014) Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet 15(1):7–21. doi:10.1038/nrg3606 PubMedView ArticleGoogle Scholar
  7. Wang KX, Chang HY (2011) Molecular mechanisms of long noncoding RNAs. Mol Cell 43(6):904–914. doi:10.1038/hr.2015.26 PubMed CentralPubMedView ArticleGoogle Scholar
  8. Tay Y, Rinn J, Pandolfi PP (2014) The multilayered complexity of ceRNA crosstalk and competition. Nature 505(7483):344–352PubMed CentralPubMedView ArticleGoogle Scholar
  9. Kornienko AE, Guenzl PM, Barlow DP, Pauler FM (2013) Gene regulation by the act of long non-coding RNA transcription. BMC Biol 11:59PubMed CentralPubMedView ArticleGoogle Scholar
  10. Maass PG, Luft FC, Bahring S (2014) Long non-coding RNA in health and disease. J Mol Med 92(4):337–346. doi:10.1007/s00109-014-1131-8 PubMedView ArticleGoogle Scholar
  11. Qiu MT, Hu JW, Yin R, Xu L (2013) Long noncoding RNA: an emerging paradigm of cancer research. Tumour Biol 34(2):613–620PubMedView ArticleGoogle Scholar
  12. Prensner JR, Chinnaiyan AM (2011) The emergence of lncRNAs in cancer biology. Cancer Discov 1(5):391–407PubMed CentralPubMedView ArticleGoogle Scholar
  13. Tang JY, Lee JC, Chang YT, Hou MF, Huang HW, Liaw CC et al (2013) Long noncoding RNAs-related diseases, cancers, and drugs. Sci World J 2013:943539. doi:10.1155/2013/943539 Google Scholar
  14. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ et al (2010) Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464(7291):1071–1076PubMed CentralPubMedView ArticleGoogle Scholar
  15. Ge X, Chen Y, Liao X, Liu D, Li F, Ruan H et al (2013) Overexpression of long noncoding RNA PCAT-1 is a novel biomarker of poor prognosis in patients with colorectal cancer. Med Oncol 30(2):588PubMedView ArticleGoogle Scholar
  16. Hauptman N, Glavac D (2013) Long non-coding RNA in cancer. Int J Mol Sci 14(3):4655–4669PubMed CentralPubMedView ArticleGoogle Scholar
  17. Zhang H, Chen Z, Wang X, Huang Z, He Z, Chen Y (2013) Long non-coding RNA: a new player in cancer. J Hematol Oncol 6:37PubMed CentralPubMedView ArticleGoogle Scholar
  18. Zhang W, Huang C, Gong Z, Zhao Y, Tang K, Li X et al (2013) Expression of LINC00312, a long intergenic non-coding RNA, is negatively correlated with tumor size but positively correlated with lymph node metastasis in nasopharyngeal carcinoma. J Mol Histol 44(5):545–554PubMedView ArticleGoogle Scholar
  19. Li J, Chen Z, Tian L, Zhou C, He MY, Gao Y et al (2014) LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma. Gut 63(11):1700–1710PubMed CentralPubMedView ArticleGoogle Scholar
  20. Chen J, Wang R, Zhang K, Chen LB (2014) Long non-coding RNAs in non-small cell lung cancer as biomarkers and therapeutic targets. J Cell Mol Med 18(12):2425–2436. doi:10.1111/jcmm.12431 PubMed CentralPubMedView ArticleGoogle Scholar
  21. Ji P, Diederichs S, Wang W, Boing S, Metzger R, Schneider PM et al (2003) MALAT-1, a novel noncoding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer. Oncogene 22(39):8031–8041PubMedView ArticleGoogle Scholar
  22. White NM, Cabanski CR, Silva-Fisher JM, Dang HX, Govindan R, Maher CA (2014) Transcriptome sequencing reveals altered long intergenic non-coding RNAs in lung cancer. Genome Biol 15(8):429PubMed CentralPubMedView ArticleGoogle Scholar
  23. Nie FQ, Zhu Q, Xu TP, Zou YF, Xie M, Sun M et al (2014) Long non-coding RNA MVIH indicates a poor prognosis for non-small cell lung cancer and promotes cell proliferation and invasion. Tumour Biol 35(8):7587–7594. doi:10.1007/s13277-014-2009-7 PubMedView ArticleGoogle Scholar
  24. Zhang XQ, Sun S, Lam KF, Kiang KM, Pu JK, Ho AS et al (2013) A long non-coding RNA signature in glioblastoma multiforme predicts survival. Neurobiol Dis 58:123–131PubMedView ArticleGoogle Scholar
  25. Hu Y, Chen HY, Yu CY, Xu J, Wang JL, Qian J et al (2014) A long non-coding RNA signature to improve prognosis prediction of colorectal cancer. Oncotarget 5(8):2230–2242PubMed CentralPubMedGoogle Scholar
  26. Meng J, Li P, Zhang Q, Yang Z, Fu S (2014) A four-long non-coding RNA signature in predicting breast cancer survival. J Exp Clin Cancer Res 33(1):84PubMed CentralPubMedView ArticleGoogle Scholar
  27. Zhao W, Luo J, Jiao S (2014) Comprehensive characterization of cancer subtype associated long non-coding RNAs and their clinical implications. Sci Rep 4:6591PubMed CentralPubMedView ArticleGoogle Scholar
  28. Yang J, Lin J, Liu T, Chen T, Pan S, Huang W et al (2014) Analysis of lncRNA expression profiles in non-small cell lung cancers (NSCLC) and their clinical subtypes. Lung Cancer 85(2):110–115PubMedView ArticleGoogle Scholar
  29. Botling J, Edlund K, Lohr M, Hellwig B, Holmberg L, Lambe M et al (2013) Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin Cancer Res Off J Am Assoc Cancer Res 19(1):194–204. doi:10.1158/1078-0432.CCR-12-1139 View ArticleGoogle Scholar
  30. Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R et al (2012) Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 72(1):100–111. doi:10.1158/0008-5472.CAN-11-1403 PubMedView ArticleGoogle Scholar
  31. Der SD, Sykes J, Pintilie M, Zhu CQ, Strumpf D, Liu N et al (2014) Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer. 9(1):59–64. doi:10.1097/JTO.0000000000000042 Google Scholar
  32. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31(4):e15PubMed CentralPubMedView ArticleGoogle Scholar
  33. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4(2):249–264PubMedView ArticleGoogle Scholar
  34. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F et al (2012) GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 22(9):1760–1774. doi:10.1101/gr.135350.111 PubMed CentralPubMedView ArticleGoogle Scholar
  35. Zhang X, Sun S, Pu JK, Tsang AC, Lee D, Man VO et al (2012) Long non-coding RNA expression profiles predict clinical phenotypes in glioma. Neurobiol Dis 48(1):1–8PubMedView ArticleGoogle Scholar
  36. Lossos IS, Czerwinski DK, Alizadeh AA, Wechser MA, Tibshirani R, Botstein D et al (2004) Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N Engl J Med 350(18):1828–1837PubMedView ArticleGoogle Scholar
  37. da Huang W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13PubMed CentralView ArticleGoogle Scholar
  38. Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, Pedraza V, Boyero L, Rosell R et al (2011) Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer. Int J Cancer 129(2):355–364. doi:10.1002/ijc.25704 PubMedView ArticleGoogle Scholar
  39. Guo NL, Wan YW (2012) Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival. Artif Intell Med 55(2):97–105PubMed CentralPubMedView ArticleGoogle Scholar
  40. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 56(2):337–344PubMedView ArticleGoogle Scholar
  41. Merico D, Isserlin R, Stueker O, Emili A, Bader GD (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5(11):e13984. doi:10.1371/journal.pone.0013984 PubMed CentralPubMedView ArticleGoogle Scholar
  42. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. doi:10.1101/gr.1239303 PubMed CentralPubMedView ArticleGoogle Scholar
  43. Kratz JR, Jablons DM (2009) Genomic prognostic models in early-stage lung cancer. Clin Lung Cancer. 10(3):151–157PubMedView ArticleGoogle Scholar
  44. Zhu CQ, Pintilie M, John T, Strumpf D, Shepherd FA, Der SD et al (2009) Understanding prognostic gene expression signatures in lung cancer. Clin Lung Cancer. 10(5):331–340PubMedView ArticleGoogle Scholar
  45. Gibb EA, Vucic EA, Enfield KS, Stewart GL, Lonergan KM, Kennett JY et al (2011) Human cancer long non-coding RNA transcriptomes. PLoS One 6(10):e25915PubMed CentralPubMedView ArticleGoogle Scholar
  46. Su X, Malouf GG, Chen Y, Zhang J, Yao H, Valero V et al (2014) Comprehensive analysis of long non-coding RNAs in human breast cancer clinical subtypes. Oncotarget. 5(20):9864–9876PubMed CentralPubMedGoogle Scholar
  47. Du Z, Fei T, Verhaak RG, Su Z, Zhang Y, Brown M et al (2013) Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer. Nat Struct Mol Biol 20(7):908–913PubMed CentralPubMedView ArticleGoogle Scholar
  48. Subramanian J, Simon R (2010) Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J Natl Cancer Inst 102(7):464–474PubMed CentralPubMedView ArticleGoogle Scholar
  49. Skarin AT, Herbst RS, Leong TL, Bailey A, Sugarbaker D (2001) Lung cancer in patients under age 40. Lung Cancer 32(3):255–264PubMedView ArticleGoogle Scholar
  50. Hsu CL, Chen KY, Shih JY, Ho CC, Yang CH, Yu CJ et al (2012) Advanced non-small cell lung cancer in patients aged 45 years or younger: outcomes and prognostic factors. BMC Cancer 12:241PubMed CentralPubMedView ArticleGoogle Scholar
  51. Chen KY, Chang CH, Yu CJ, Kuo SH, Yang PC (2005) Distribution according to histologic type and outcome by gender and age group in Taiwanese patients with lung carcinoma. Cancer 103(12):2566–2574PubMedView ArticleGoogle Scholar
  52. Kalemkerian GP, Akerley W, Bogner P, Borghaei H, Chow LQ, Downey RJ et al (2013) Small cell lung cancer. J Natl Compr Canc Netw 11(1):78–98PubMed CentralPubMedGoogle Scholar
  53. Hangauer MJ, Vaughn IW, McManus MT (2013) Pervasive transcription of the human genome produces thousands of previously unidentified long intergenic noncoding RNAs. PLoS Genet 9(6):e1003569PubMed CentralPubMedView ArticleGoogle Scholar

Copyright

© Zhou et al. 2015

Advertisement